In physics ML and ANNs have been applied in condense matter, statistical physics, cold atoms, quantum many-body theory, quantum computing, cosmology, particle physics or nuclear physics (see Refs. [1]}, [2]}, [3]} and references therein for recent reviews on the application of ML and ANNs in physics). In particular, applications in nuclear physics [4]}, [5]} date back to the beginning of the 90's when Gazula et al., [6]} employed a
feed-forward neural network to study global nuclear properties across the nuclear landscape. Since then, ML and ANNs have been used to predict among other things nuclear masses [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, charge radii [16]}, [17]}, [18]}, [19]}, \(\alpha \) - and \(\beta \) -decay half-lives [20]}, [21]}, [22]}, [23]}, [24]}, fission yields [25]}, [26]}, [27]}, [28]}, [29]}, fusion reaction cross-sections [30]}, isotropic cross-sections in proton-induced spallation reactions [31]}, [32]}, ground-state and excited energies [33]}, dripline locations [34]}, [35]}, the deuteron properties [36]}, the nuclear liquid-gas phase transition [37]}, nuclear energy density functionals [38]} or neutron star properties and the nuclear matter equation of state [39]}, [40]}, [41]}, [42]}, [43]}.
